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A Two-Stage Dominance-Based Surrogate-Assisted Evolution Algorithm for High-Dimensional Expensive Multi-Objective Optimization

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Titel: A Two-Stage Dominance-Based Surrogate-Assisted Evolution Algorithm for High-Dimensional Expensive Multi-Objective Optimization
Autoren: Wanliang Wang, Mengjiao Yu, Rui Dai, Zhongkui Chen
Verlagsinformationen: Research Square Platform LLC, 2023.
Publikationsjahr: 2023
Beschreibung: In the past decades, surrogate-assisted evolutionary algorithms (SAEAs) have become one of the most popular methods to solve expensive multi-objective optimization problems (EMOPs). However, most existing methods focus on low-dimensional EMOPs because a large number of training samples are required to build accurate surrogate models, which is unrealistic for high-dimensional EMOPs. Therefore, this paper develops a two-stage dominance-based surrogate-assisted evolution algorithm for high-dimensional expensive multi-objective optimization (TSDEA) which utilizes the RBF model to approximate each objective function. First, a two-stage selection strategy is applied to select individuals for re-evaluation. Then considering the training time of the model, proposing a novel archive updating strategy to limit the number of individuals for updating. Experimental results show that the proposed algorithm has promising performance and computational efficiency compared to the state-of-the-art five SAEAs.
Publikationsart: Article
DOI: 10.21203/rs.3.rs-2638614/v1
Rights: CC BY
Dokumentencode: edsair.doi...........a6b3fb2a3441b2010aaa20c097eabd72
Datenbank: OpenAIRE
Beschreibung
Abstract:In the past decades, surrogate-assisted evolutionary algorithms (SAEAs) have become one of the most popular methods to solve expensive multi-objective optimization problems (EMOPs). However, most existing methods focus on low-dimensional EMOPs because a large number of training samples are required to build accurate surrogate models, which is unrealistic for high-dimensional EMOPs. Therefore, this paper develops a two-stage dominance-based surrogate-assisted evolution algorithm for high-dimensional expensive multi-objective optimization (TSDEA) which utilizes the RBF model to approximate each objective function. First, a two-stage selection strategy is applied to select individuals for re-evaluation. Then considering the training time of the model, proposing a novel archive updating strategy to limit the number of individuals for updating. Experimental results show that the proposed algorithm has promising performance and computational efficiency compared to the state-of-the-art five SAEAs.
DOI:10.21203/rs.3.rs-2638614/v1